TY - GEN
T1 - An intelligent diagnosis method for Chronis hepatitis B in TCM
AU - Chu, Na
AU - Che, Zhiying
AU - Zhou, Min
AU - Zhao, Yu
AU - Ma, Lizhuang
PY - 2013
Y1 - 2013
N2 - In traditional Chinese medicine (TCM), it is frequently found that more than one syndrome of a patient are recognized in clinical practice, which has its own symptoms and signs. While, most algorithms are used to solve issues of syndrome diagnosis that only focus on one syndrome. Therefore, we proposed a hybrid intelligent syndrome diagnosis (HISD) model. Methods. The HTSD model combined feature selection methods to select the significant symptoms and signs corresponding to syndromes of CHB, and combined probability-classification methods to obtain the main syndrome and accompanying syndromes. The model was carried on 664 records of CHB. Results. 16 features were selected for the syndrome of Damp Heat in the Liver and Gallbladder (DHLG), 20 features were selected for the syndrome of Liver qi Stagnation and Spleen Deficiency (LSSD) and 13 features were selected for the syndrome of Yin Deficiency of Liver and Kidney (YDLK). The lowest average accuracy was 80.52% using logitboost, whereas the accuracy of HISD was 85% for unrecognized cases of CHB. Conclusion. Our method extracts the relevant symptoms and signs for each syndrome, recognizes the main syndrome and accompanying syndromes, and improves its recognition accuracy.
AB - In traditional Chinese medicine (TCM), it is frequently found that more than one syndrome of a patient are recognized in clinical practice, which has its own symptoms and signs. While, most algorithms are used to solve issues of syndrome diagnosis that only focus on one syndrome. Therefore, we proposed a hybrid intelligent syndrome diagnosis (HISD) model. Methods. The HTSD model combined feature selection methods to select the significant symptoms and signs corresponding to syndromes of CHB, and combined probability-classification methods to obtain the main syndrome and accompanying syndromes. The model was carried on 664 records of CHB. Results. 16 features were selected for the syndrome of Damp Heat in the Liver and Gallbladder (DHLG), 20 features were selected for the syndrome of Liver qi Stagnation and Spleen Deficiency (LSSD) and 13 features were selected for the syndrome of Yin Deficiency of Liver and Kidney (YDLK). The lowest average accuracy was 80.52% using logitboost, whereas the accuracy of HISD was 85% for unrecognized cases of CHB. Conclusion. Our method extracts the relevant symptoms and signs for each syndrome, recognizes the main syndrome and accompanying syndromes, and improves its recognition accuracy.
KW - Traditional Chinese medicine
KW - data mining
UR - https://www.scopus.com/pages/publications/84894518346
U2 - 10.1109/BIBM.2013.6732629
DO - 10.1109/BIBM.2013.6732629
M3 - 会议稿件
AN - SCOPUS:84894518346
SN - 9781479913091
T3 - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
SP - 20
EP - 22
BT - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
T2 - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Y2 - 18 December 2013 through 21 December 2013
ER -